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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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域自适应水下物体检测通过补充风格意识学习.

Xinmiao Gao1, Miao Yang1, Zhuoran Xie2

  • 1School of Electronic Engineering, Jiangsu Ocean University, LianYunGang, China.

Neural networks : the official journal of the International Neural Network Society
|October 17, 2025
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概括
此摘要是机器生成的。

这项研究引入了一种新的互补风格意识的平均教师 (CSAMT) 模型,以改善水下物体检测. 通过解脱风格和内容,CSAMT提高了伪标签的质量,在跨领域的场景中实现了最先进的性能.

关键词:
域名适应领域适应图像对 图像对 图像对平均教师是指教师.水下物体检测系统是水下物体检测系统.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 由于水质,照明和成像设备,水下图像呈现多种风格的变化,导致域间隙.
  • 这个域间隙,加上有限的注释数据,在跨域任务中降低了对象检测性能.
  • 现有的Mean Teacher框架与伪标签质量扎,限制了它们在跨域对象检测中的有效性.

研究的目的:

  • 提出一种补充风格意识的平均教师 (CSAMT) 模型,以提高跨域对象检测性能.
  • 为了解决水下图像的传统平均教师框架中伪标签生成的局限性.
  • 提高在各种水下环境中对象检测的稳定性和准确性.

主要方法:

  • 构建图像对来进行风格意识学习,并使用WCT2.2进行风格内容解.
  • 利用离散波纹转换 (DWT) 实现跨频率和空间域的联合特征建模.
  • 引入一个两阶段的教师-学生区域提案对齐 (TTRPA) 战略,以改善注意力和一致性损失.

主要成果:

  • 拟议的CSAMT模型在三个领域适应基准上实现了最先进的性能.
  • 废弃性研究证实了CSAMT模型的每个组成部分的显著贡献.
  • 该模型在具有挑战性的跨领域水下场景中展示了改进的物体检测准确性.

结论:

  • 通过提高伪标签质量,CSAMT模型有效地弥合了水下物体检测领域的差距.
  • 风格意识学习和TTRPA对于在跨领域环境中提高平均教师框架的性能至关重要.
  • 这项研究为在多样化和具有挑战性的水下环境中进行强大的物体检测提供了有希望的方法.